Accelerating human understanding of medical images by dynamic image alteration

ABSTRACT

A method, computer system, and a computer program product for dynamically altering at least one image is provided. The present invention may include receiving a plurality of data, wherein the received plurality of data includes at least one existing medical image. The present invention may also include determining that one or more user instructions for the received existing image were received. The present invention may then include implementing the one or more user instructions on the received existing medical image. The present invention may also include altering the received existing medical image based on the one or more implemented user instructions and a medical knowledge base.

BACKGROUND

The present invention relates generally to the field of computing, andmore particularly to cognitive computing.

The development of expertise utilizes a series of events: demonstration,imitation with correction, and repetition. Ultimately, an expertacquires gestalt (i.e., the ability to rapidly recognize normality andabnormality via a global appreciation of visual input). As a result, theexpert processes visual input (i.e., images), including the recognitionof salient imaging finding differently than less-skilled individuals.

The development of intuitive recognition of images typically requiresyears of training, and is often referred to as the development ofgestalt. Training provides frequent close-loop feedback in whichabnormalities and normalities may be understood in relation to either acomparable image or mental expectation.

SUMMARY

Embodiments of the present invention disclose a method, computer system,and a computer program product for dynamically altering at least oneimage based on the system having an underlying understanding of theimaging manifestations of one or more diseases and receiving userinstruction as either user input or acting on stored image presentationrules (i.e., configurable rules or previously determined user rules).The present invention may include receiving a plurality of data, whereinthe received plurality of data includes at least one existing medicalimage. The present invention may also include determining that one ormore user instructions for the received existing image were received.The present invention may then include implementing the one or more userinstructions on the received existing medical image. The presentinvention may also include altering the received existing medical imagebased on the one or more implemented user instructions and a medicalknowledge base.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is an operational flowchart illustrating a process fordynamically altering images based on user instruction (i.e., previouslydetermined user rules and/or user input) according to at least oneembodiment;

FIG. 3 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 4 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, in accordance with anembodiment of the present disclosure; and

FIG. 5 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 4, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language, Python programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following described exemplary embodiments provide a system, methodand program product for dynamically altering at least one image. Assuch, the present embodiment has the capacity to improve the technicalfield of cognitive computing by dynamically altering images based onuser interaction (i.e., user rules and/or user input). Morespecifically, the dynamic image alteration program may receive data(i.e., textual and visual data) associated with a specific patient. Thedynamic image alteration program may then display the existing imagesassociated with the patient to the user. If the dynamic image alterationprogram determines that the user previously provided rules, then thedynamic image alteration program may apply these previously determineduser rules (i.e., configurable rules) and then proceed to determinewhether user input existed and apply existing user input. If, however,the dynamic image alteration program failed to provide any user rules,then the dynamic image alteration program may proceed to apply userinput. The dynamic image alteration program may then retrieve imagesfrom a deep learning (e.g., generative adversarial network) and imageanalysis engine with a medical knowledge base and may generate at leastone altered image based on the previously determined user rules and/oruser input. The dynamic image alteration program may then determinewhether the user has additional user input, which may be utilized togenerate additional altered images (i.e., secondary altered images).

As described previously, the development of expertise utilizes a seriesof events: demonstration, imitation with correction, and repetition.Ultimately, an expert acquires gestalt (i.e., the ability to rapidlyrecognize normality and abnormality via a global appreciation of visualinput). As a result, the expert processes visual input (i.e., images),including the recognition of salient imaging finding differently thanless-skilled individuals.

The development of intuitive recognition of images typically requiresyears of training, and is often referred to as the development ofgestalt. Training provides frequent close-loop feedback in whichabnormalities and normalities may be understood in relation to either acomparable image or mental expectation.

Machine learning algorithms may create images through generativeadversarial networks. A large amount of data may be gathered and themodel may be trained to generate similar data. The neural networksutilized in generative models may have significantly fewer parametersthan the generative model that the data may be trained on. As such, thegenerative models may discover and generate the critical portions of thedata.

Therefore, it may be advantageous to, among other things, utilize deeplearning and image analytics to generate abnormalities on images inresponse to user input or rules, especially in the healthcare field. Inthe healthcare field of medical imaging, training doctors typicallytakes several years, and even seasoned professionals require ongoingtraining. Therefore, a system that can accelerate the development ofgestalt expertise may accelerate and improve training, as well as beuseful in daily practice to assist medical personnel and medicalprofessionals to recognize imaging manifestations of disease and normalconditions.

In the present embodiment, the dynamic image alteration program mayutilize deep learning and image analytics to generate abnormalities onimages in response to user input or rules. Deep learning is a subfieldof machine learning associated with known algorithms, inspired by thestructure and function of the neural network in the animal visualcortex. Deep learning may utilize a neural network to classify orcluster images. As deep learning is utilized more frequently to assessimages, computerized systems that analyze the images may learn toreceive features and findings that depict various abnormalities (e.g.,diseases, mutations). In addition to analyzing the images, thecomputerized systems may modify the images to depict specific normal andabnormal findings. For example, a radiologist may examine thousands ofchest radiographs in teaching files or actual practice in order todevelop an understanding of the various manifestations of congestiveheart failure. Over time, the radiologist may develop the ability todiagnose congestive heart failure on radiographs by gestalt. The sameradiologist may similarly review thousands of radiographs to understandthe appearances of pulmonary edema. After the review of several thousandexamples, the radiologist may learn how to differentiate pulmonary edemafrom other causes of thickened lung interstitial tissues. As such, aphysician in training or in-practice may benefit from a computerizeddisplay of a spectrum of diseases or other anatomical variations. Insome cases, these abnormalities may be dynamically created on images ofa particular patient that is the subject of an imaging examination.

According to at least one embodiment, the dynamic image alterationprogram may utilize artificial intelligence and a knowledge base with acomputerizing medical image display program (e.g., picture archiving andcommunication system (PACS), a medical imaging technology that provideseconomical storage and convenient access to images from multiplemodalities) to present images that may be controlled by the user, andare based on real (i.e., existing) and generated images. In the dynamicimage alteration program, the same images may be dynamically modified todisplay an increase or decrease in the severity of one or multiplediseases, a progression or regression of one or multiple diseases, thedisease flipped from left to right side, or a continuation of certainactivities (e.g., risky behaviors or habits) by an individual. Thedynamic image alteration program may include the modification of animage to show different disease states providing the comparison of eachimage side-by-side, or via alternative comparative display means (e.g.,viewing the images on different image viewers), or displaying amovie-like dynamic alteration of an image. The dynamic image alterationprogram may utilize automated rules to control the display of createdimages and their arrangement relative to the existing images of thespecific patient. The automated rules may be responsive to the user,exam indications, patient demographics, location and site configuration.The dynamic image alteration program may also retrieve, generate ordisplay images of other body regions to show the user othermanifestations of systemic diseases that may affect multiple organs.

The present embodiment may be integrated into another program togenerate differential diagnosis to automatically show the most likelysymptoms and effects of various diseases in the differential diagnosis.

The present embodiment may be utilized to create new anatomicalfindings. The dynamic image alteration program may be triggered bypreviously determined user rules, user input or both (i.e., userinteraction) to generate an image with an altered anatomy. According toat least one embodiment, the dynamic image alteration program may altera portion of an image based on user interaction (i.e., user rules and/oruser input).

According to at least one embodiment, the dynamic image alterationprogram may understand the features and findings of various relateddiseases on chest radiographs, including various different views (e.g.,posteroanterior (PA), anteroposterior (AP), lateral, supine, upright anddecubitus view) in patients of various age, gender, race, and stature,and the dynamic image alteration program may produce a spectrum offindings in response to user input or programmed rules. For example, aradiologist using a PACS may start with a normal chest upright PA chestradiograph and slowly dial-in early congestive heart failure, moderate,or severe congestive heart failure with pulmonary edema, thennon-cardiogenic pulmonary edema of various causes, then interstitiallung disease of various causes. In a short period of time, the physicianmay observe, with real-time feedback, a spectrum of cognitiveexperiences that may otherwise take years to obtain and compare. Thisprocess may be repeated with supine chest radiographs, AP vs. PA views,young patients, or old patients. As such, years of training may becompressed into minutes or hours.

According to at least one embodiment, the dynamic image alterationprogram may include the dynamic modification of at least one image basedon the specific imaging exam or group of exams as driven by a user inputor rule, or various information associated with the exam or patient.Such dynamic displays may increase both the specificity and sensitivityof detecting and distinguishing various abnormalities and may acceleratethe development of user experience and the intuitive decision making ofthe user (e.g., gestalt). For example, a radiologist or other clinicalexpert may be provided a chest radiograph with a history of possiblesarcoidosis. The dynamic image alteration program may not only displaythe image, but the dynamic image alteration program may also show whatsarcoidosis may look like on this particular image. The dynamic imagealteration program may enable the user to display various manifestationsand stages of the disease, and the dynamic image alteration program mayshow what the exam would look like if automatically detected or manuallyspecified abnormalities were removed. In the present embodiment, thedynamic image alternation program may continue to train the user withgenerative images until the user achieves a certain level of sensitivityand specificity.

According to at least one embodiment, the dynamic image alterationprogram may create a series of altered images showing themanifestations, stages and severity of an abnormality, or displayaltered images on the effect of the abnormality. Such altered images mayassist an expert to detect abnormalities, and the various manifestationsresulting from the abnormalities, as well as the effects of theabnormalities. For example, in the case of sarcoidosis, the dynamicimage alteration program may visually show skeletal or cerebralmanifestations that may result from the disease, or how the disease inthe chest may appear when it progresses or regresses. The dynamic imagealteration program may also show other manifestations of the disease inother organs.

According to at least one embodiment, the dynamic image alterationprogram may be utilized to assist non-specialists by displayingcomparable images to the user. The user may request that the dynamicimage alteration program generate multiple images for comparison. Assuch, the user may compare images in whole or part with another imagefrom the same or another source, or a stored image or non-visualrepresentation. For example, the dynamic image alteration program orassociated program may use data from the patient's medical record andprovide a differential diagnosis, then the dynamic image alterationprogram may alter the images to show how various diagnosticpossibilities might be manifested on the particular images being viewed.In another example, the dynamic image alteration program may identify afinding on a skeletal radiograph that may indicate the differentialdiagnosis includes non-displaced fracture, nutrient vessel canal, orartifact. Using the dynamic image alteration program, the user may alterthe image to show a typical non-displaced fracture, a typical nutrientcanal, or examples of common artifacts that may masquerade as suchpathology to help the user appreciate which of the diagnosticpossibilities comes closest to the current appearance. The dynamic imagealteration program may alter the images currently being viewed to supplya reference standard for optimal comparison.

According to at least one embodiment, the dynamic image alterationprogram may be utilized for individual education or behaviormodification. The user may generate comparable images in an attempt tomodify the behavior of another individual (e.g., patient). For example,a patient who is a smoker may have a chest radiograph showing minimalabnormalities. Utilizing the dynamic image alteration program, thepatient, who is a smoker, may be shown the progression of disease thatmay be expected if the patient continues to smoke.

The present embodiment may be utilized in various fields, such asmedical imaging, and non-static images (e.g., video or movies).According to at least one embodiment, the dynamic image alterationprogram may be utilized by the user to develop the skill of rapidlyresponding to images in various professions (e.g., pilots, firefighters, police officers, physicians, drivers). For example, bydynamically altering images of a normal body part, the dynamic imagealteration program may help train a doctor to recognize the earliestmanifestations of a disease, not just the later stages when themanifestations are more obvious. Similarly, the dynamic image alterationprogram may be used to train a fighter pilot or professional quarterback(i.e., American football player).

The present embodiment may include a database to store and index imagesbased on the user preferences in a specific domain. For example, in thefield of medical imaging, a database of medical images with knowndisease types may be established and indexed with patient demographics(e.g., age, height, weight, sex, ethnicity), image modality (x-ray, MRI,ultrasound) and orientation for two dimensional images (e.g., AP x-ray,lateral x-ray) or for cross-sectional images (e.g., axial, coronal,sagittal). The images with specific disease types may be organized intodemographic groups, such as Caucasian females aged 50-55 withHepatocellular Carcinoma (HCC). As such, the user may open a medicalimage for viewing and may select a specific disease type, or if intraining mode, the dynamic image alteration program may select a diseasetype. According to at least one embodiment, for training or researchpurposes, the dynamic image alteration program may determine the patientdemographic, image type and laterality through information in thehospital electronic medical record and information with the image header(e.g., digital imaging and communications in medicine (DICOM) header)related to the existing images. The generated information may beutilized to identify existing images within the current patient'sdemographic, and an altered image depicting the disease corresponding tothe existing images may be established.

According to at least one embodiment, the dynamic image alterationprogram may register the altered images to a current individual orcircumstance. The dynamic image alteration program may compute an imageregistration, for example, by solving a non-convex, non-linearoptimization problem balancing two competing forces: (1) the similarityof fixed and warped images (e.g., mutual information); and (2) thesmoothness or invertibility of the vector deformation field. Onceregistered, the user may modify the blending or comparison between thecurrent images and the abnormal images by viewing the imagesside-by-side, or alternatively by toggling or shuffling back and forthbetween the images.

According to at least one embodiment, the dynamic image alterationprogram may alternate between the discriminator and generator fortraining purposes (i.e., generative adversarial networks). The generatorparameter weights may be fixed to train the discriminator to distinguishbetween real and generated images. The error between the real andgenerated images may be the loss that is fed back (i.e., backpropagated) to update the discriminator weights. The discriminatorparameter weights may be fixed as the generator creates samples. Theerror may be fed back to update the generator weights. Both thegenerator parameter weights and discriminator parameter weights may beiterated until convergence. A successful result may be based on whetherthe discriminator is unable to distinguish the difference between thereal and generated images created by the generator (e.g., Turing test).

According to at least one embodiment, in the medical imaging field, thedynamic image alteration program may apply deep learning to medicalimages to determine features that depict anatomy. The dynamic imagealteration program may then review medical literature about diseases ina deep learning neural network. The dynamic image alteration program maythen index data about the various disease states and diseased imagefeatures. The dynamic image alteration program may then obtain a newexam. The dynamic image alteration program may then match the examimages to similar anatomy with the specified disease based on the userrule or input. The dynamic image alteration program may then registerand deform to alter the existing image to match the features of aparticular disease. The dynamic image alteration program may then enablethe user to input the severity of the disease.

According to at least one embodiment, in the medical imaging field, thedynamic image alteration program may apply deep learning or otherartificial intelligence to medical images to determine features thatdepict anatomy. The dynamic image alteration program may then reviewmedical literature about diseases in a deep learning neural network. Thedynamic image alteration program may then index data about the variousdisease states and diseased image features. The dynamic image alterationprogram may then obtain a new exam. During a pre-reading or readingprocess, the dynamic image alteration program may then generate imagesto show various manifestations and stages of one or more diseases peruser rule or input by altering an existing image. The dynamic imagealteration program may then provide a user input or configurable rulesfor the user to display various images, which may include an option tostore rules to determine when and how these modified images areautomatically displayed, or generate a search or input process (e.g.,extracting information about the reason for the exam or patient'sdifferential diagnosis) to determine which images are displayed.

Referring to FIG. 1, an exemplary networked computer environment 100 inaccordance with one embodiment is depicted. The networked computerenvironment 100 may include a computer 102 with a processor 104 and adata storage device 106 that is enabled to run a software program 108and a dynamic image alteration program 110 a. The networked computerenvironment 100 may also include a server 112 that is enabled to run adynamic image alteration program 110 b that may interact with a database114 and a communication network 116. The networked computer environment100 may include a plurality of computers 102 and servers 112, only oneof which is shown. The communication network 116 may include varioustypes of communication networks, such as a wide area network (WAN),local area network (LAN), a telecommunication network, a wirelessnetwork, a public switched network and/or a satellite network. It shouldbe appreciated that FIG. 1 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The client computer 102 may communicate with the server computer 112 viathe communications network 116. The communications network 116 mayinclude connections, such as wire, wireless communication links, orfiber optic cables. As will be discussed with reference to FIG. 3,server computer 112 may include internal components 902 a and externalcomponents 904 a, respectively, and client computer 102 may includeinternal components 902 b and external components 904 b, respectively.Server computer 112 may also operate in a cloud computing service model,such as Software as a Service (SaaS), Analytics as a Service (AaaS),Platform as a Service (PaaS), or Infrastructure as a Service (IaaS).Server 112 may also be located in a cloud computing deployment model,such as a private cloud, community cloud, public cloud, or hybrid cloud.Client computer 102 may be, for example, a mobile device, a telephone, apersonal digital assistant, a netbook, a laptop computer, a tabletcomputer, a desktop computer, or any type of computing devices capableof running a program, accessing a network, and accessing a database 114.According to various implementations of the present embodiment, thedynamic image alteration program 110 a, 110 b may interact with adatabase 114 that may be embedded in various storage devices, such as,but not limited to a computer/mobile device 102, a networked server 112,or a cloud storage service.

According to the present embodiment, a user using a client computer 102or a server computer 112 may use the dynamic image alteration program110 a, 110 b (respectively) to dynamically alter an image based on userrules and actions. The dynamic image alteration method is explained inmore detail below with respect to FIG. 2.

Referring now to FIG. 2, an operational flowchart illustrating theexemplary image alteration process 200 used by the dynamic imagealteration program 110 a and 110 b according to at least one embodimentis depicted.

At 202, data is received by the dynamic image alteration program 110 a,110 b. Using a software program 108 on the user's device (e.g., user'scomputer 102), data may be received as input from a known computerizedmedical image display program (e.g., picture archiving and communicationsystem (PACS), which provides economical storage and convenient accessto images from multiple modalities) to the dynamic image alterationprogram 110 a, 110 b via communications network 116. The data mayinclude visual data (e.g., x-rays, scans, graphs), and textual data(e.g., medical records without images) associated with a patient. Thetextual data may be utilized to identify the images (i.e., existingimages) located in the received data, or to extract assertions orconnections between the existing images. Alternatively, the user mayalso manually input visual and textual data into the dynamic imagealteration program 110 a, 110 b, without the use of a computerizedmedical image display program.

For example, a patient, a 39-year-old female, was diagnosed withanaplastic astrocytoma (i.e., a grade III brain tumor that develops fromstar-shaped astrocytes that support nerve cells) in the right cerebralhemisphere. A radiation oncology resident (i.e., resident), with verylimited experience with brain tumor cases, is assisting a team ofphysicians, which includes neurosurgeons, oncologists and a seniorradiation oncologist, to treat the patient. As such, the residentutilizes the dynamic image alteration program 110 a, 110 b to gain moreexperience on the progression and stages of anaplastic astrocytoma. Theresident obtains the patient's medical records including computedtomography scans (CT scans), magnetic resonance imaging scans (MRIscans), x-rays, physician notes and reports from a PACS, and uploads thepatient's medical records, via an encrypted virtual server, into thedynamic image alteration program 110 a, 110 b.

In the present embodiment, the existing images may be displayed by thecomputerized medical image display program. Using the data received from202, the computerized medical image display program may display theexisting images received before determining the user instruction (i.e.,configurable rules/previously determined user rules and/or user input)that may be applied (i.e., implemented) on the existing images. Theuser, for example, may view existing images, such as CT and MRI scans,utilizing the computerized medical image display image program, prior tocreating alterations to the existing image.

Then, at 204, the dynamic image alteration program 110 a, 110 bdetermines if there are previously determined user rules. Before theuser receives data at 202, the user may provide rules to the dynamicimage alteration program 110 a, 110 b. At the bottom of the screen,there is a “User Rules” button displayed. Once the user clicks on the“User Rules” button, the user may be prompted (e.g., via a dialog box)to include rules to alter (i.e., anatomically alter or change) theexisting image. The dialog box may include a list of categories for theuser rules (e.g., age group, medical field, body part, orientation). Tothe left of each category, there may be a box for the user to click toselect that category. To the right of the category, there may be a boxfor the user to provide additional information associated with thespecific category (e.g., if the user clicks age group, the user maydetermine which age groups may be included or excluded, or for a medicalfield, the user may determine which medical fields may be included orexcluded). Under the list of categories, there may be a box for the userto include any additional user rules. At the bottom of the dialog boxmay be a “Submit” button, which the user may click when the applicablecategories for the user rules have been made. After the “Submit” buttonis clicked, then the dialog box may then disappear.

Continuing the previous example, user rules were previously determinedsince the radiation oncology practice specializes in women's health. Assuch, the previously determined user rules are limited to women. Priorto uploading the data, the resident also set a few additional userrules. The resident clicked on the “User Rules” button located at thebottom of the main screen. A dialog box then appeared on the screen. Thedialog box included a list of categories for the resident to select toinclude additional user rules for this specific patient. The list ofcategories included age group range, specific ethnicity and specificbody part. To the left of each category was a box for the resident toselect, if the resident selects that category. To the right of eachcategory was a comment box for the resident to include more detailsrelated to the selected category. Since anaplastic astrocytoma is morecommon among people between the ages of 30 to 50, the resident decidedto limit the images to that age range. As such, the resident selectedthe box to the left of age group, and then entered “30-50 ONLY” in thecomment box located to the right of the age group category. The residentthen clicked the “Submit” button located at the bottom of the dialogbox. The dialog box then disappeared.

In the present embodiment, the dynamic image alteration program 110 a,110 b may include default user rules. The default user rules arestandard user rules that are included in every altered image. Thedefault user rules may be removed by a system administrator and not ageneral user of the dynamic image alteration program 110 a, 110 b.

In the present embodiment, the dynamic image alteration program 110 a,110 b may include recommended categories, or previously selectedcategories based on the rules previously selected by the user.Recommended categories or previously selected categories may be includedin the list of categories. Each recommended or previously selectedcategory may be designated with an asterisk.

In the present embodiment, the dynamic image alteration program 110 a,110 b may prompt the user to provide additional clarification for aselected category. When the user clicks the “Submit” button located atthe bottom of the dialog box, if the dynamic image alteration program110 a, 110 b recommends further clarification to implement a specificuser rule, then the user may be prompted (e.g., via another dialog box).The dialog box, for example, may reiterate the proposed user rule. Atthe bottom of the proposed user rule, there may be “Agree” or “Disagree”buttons which the user may select. If the user clicks the “Agree”button, then the proposed user rule may be implemented and the dialogbox may disappear. If, however, the user clicks the “Disagree” button,then another dialog box may appear for the user to provide furtherclarification. The user may be prompted to provide further clarificationto a user rule until the user clicks the “Agree” button for the proposeduser rule. After multiple attempts to clarify a proposed user rule, theuser may be prompted to cancel the user rule, or continue to providefurther clarification until the user agrees with the proposed user rule.

If the dynamic image alteration program 110 a, 110 b determines thatthere are previously determined user rules at 204, then the dynamicimage alteration program 110 a, 110 b applies the previously determineduser rules at 206. The dynamic image alteration program 110 a, 110 b mayapply these previously determined user rules to generate an alteredimage from the existing image received by the computerized medical imagedisplay program.

Continuing the previous example, the dynamic image alteration program110 a, 110 b applied the previously determined user rules that limit thegender to women and the age group to persons between 30 and 50 yearsold.

Regardless if the dynamic image alteration program 110 a, 110 bdetermines there are no previously determined user rules at 204, or thedynamic image alteration program 110 a, 110 b applied the user rules at206, the dynamic image alteration program 110 a, 110 b proceeds todetermine if there is user input at 208. The dynamic image alterationprogram 110 a, 110 b may obtain the user input using the client computer102 and prompt the user to provide user input to the dynamic imagealteration program 110 a, 110 b. The user may be prompted (e.g., via adialog box) to include user input. The dialog box, for example, may askthe user whether there is user input. Under the question, there may be“Yes” and “No” buttons for the user to select. If the user selects the“No” button for user input at 208, then the dynamic image alterationprogram 110 a, 110 b may proceed to generate an alteration of theexisting image which will be described below at 212.

Continuing the previous example, after the previously determined userrules are applied to the received medical image, a dialog box thenappeared on the screen. The dialog box asks the resident whether theresident has user input. Under the question, there are “Yes” or “No”buttons. The user selects the “Yes” button, and the dialog box thendisappears. The resident will then be prompted to provide user input.

If, however, the dynamic image alteration program 110 a, 110 bdetermines that there is user input at 208, then the dynamic imagealteration program 110 a, 110 b applies the user input at 210. A searchand input process may be utilized to determine how the existing imagewith specific findings may be altered based on user input. The user maybe prompted (e.g., via a dialog box) to provide user input in which theuser may provide input (e.g., specific input) to alter (i.e., change theanatomical findings in a medical image to show another disease, remove adisease manifestation, or change a stage or severity of a diseasemanifestation) the existing image. The dialog box, for example, mayinclude a list of proposed alterations for user input (e.g., progressionor regression of one or multiple diseases (i.e., different severity andstages of one or multiple diseases), show a different disease state,show the manifestation of the disease in other organs or organ systems,depict a specific disease type, show a specific anatomical region, showan organ absent the disease, depict a portion of the existing image,change the size of the disease). To the left of each proposedalteration, there may be a box for the user to click to select thatspecific alteration. To the right of each proposed alteration, there maybe a comment box for the user to provide additional details associatedwith the selected alteration. Under the list of proposed alterations,the dialog box may include a comment box for the user to provideadditional alterations excluded in the list of proposed alterations. Atthe bottom of the dialog box, there is a “Submit” button which the usermay click after the user has selected the specific alterations for theexisting image. After the user clicks the “Submit” button, the dialogbox may disappear.

Continuing the previous example, a dialog box to enter the user inputappears on the screen after the dynamic image alteration program 110 a,110 b applies the previously determined user rules. The dialog boxincludes a list of proposed alterations for the existing images. Theresident selects three alterations from the list: (1) progression of thedisease; (2) how the disease appears in a different state; and (3)manifestations of the disease in other organs or organ systems. As such,the resident selects the box located to the left of each proposedalteration and provides more details in the comment box located to theright of each proposed alteration. For the progression of the disease,the resident includes “indefinite” in the comment box. Therefore, theresident will view how the anaplastic astrocytoma progresses in womenbetween the ages 30 and 50 for an unlimited period of time. The residentknows that anaplastic astrocytoma grade III may advance to glioblastomagrade IV (i.e., the most common and deadliest of the malignant primarybrain tumors in adults). As such, the resident includes “glioblastomagrade IV” in the comment box to the right of the selected alteration onhow the disease appears in a different state. The resident will viewimages of glioblastoma grade IV in women between the ages 30 and 50.Since anaplastic astrocytoma may spread to other parts of the brain ornervous system, and spreads, although rarely, to other organ systemsoutside of the central nervous system, the resident includes “leftcerebral hemisphere, brain stem, spinal cord, and other organ systemsoutside of the central nervous system” in the comment box to the rightof the selected alteration on manifestations of the disease in otherorgans or organ systems. The resident will view images of anaplasticastrocytoma in the left cerebral hemisphere, the brain stem, the spinalcord and other organ systems outside of the central nervous system inwomen between the ages of 30 and 50. After selecting the three proposedalterations of the existing images, the resident clicks the “Submit”button located on the bottom of the dialog box. The dialog box thendisappears.

In the present embodiment, the user may select an automated test mode(i.e., automated test mode request). At the top of the dialog boxdisplaying the list of proposed alterations for user input, there may bean “Automated Test Mode” button. If the user clicks the “Automated TestMode” button, then the dynamic image alteration program 110 a, 110 b mayautomatically loop through different cases to test the user. Theautomated test mode may assist with the training of medical experts toidentify and diagnose different diseases, especially rare diseases. Inthe present embodiment, the dynamic image alteration program 110 a, 110b may continue in the automated test mode until the user achieves acertain result or threshold (e.g., satisfy a certain threshold ofsensitivity and specificity diagnosing a target disease).

In the present embodiment, the dynamic image alteration program 110 a,110 b may include alterations previously selected by the user (i.e.,previously selected alterations). Previously selected alterations may beincluded in the list of alterations and may be designated with anasterisk to the right of the previously selected alteration.

In the present embodiment, the dynamic image alteration program 110 a,110 b may include recommended alterations. Based on the patient'sspecific information, the dynamic image alteration program 110 a, 110 bmay recommend alterations to the existing image. The recommendedalterations may be designated with multiple asterisks to the right ofthe recommended alteration.

In the present embodiment, the dynamic image alteration program 110 a,110 b may prompt the user to provide additional clarification for aselected alteration. When the user clicks the “Submit” button located atthe bottom of the dialog box, the user may be prompted (e.g., viaanother dialog box) by the dynamic image alteration program 110 a, 110 bto provide further clarification to implement a specific alteration. Thedialog box, for example, may reiterate the proposed alteration. At thebottom of the proposed alteration, there may be “Agree” or “Disagree”buttons which the user may select. If the user clicks the “Agree”button, then the proposed alteration may be implemented and the dialogbox may disappear. If, however, the user clicks the “Disagree” button,then another dialog box may appear for the user to provide furtherclarification. The user may be prompted to provide further clarificationto an alteration until the user clicks the “Agree” button for theproposed alteration. After multiple attempts to clarify a proposedalteration, the user may be prompted to cancel the proposed alteration,or continue to provide further clarification until the user agrees withthe proposed alteration.

In the present embodiment, the dynamic image alteration program 110 a,110 b may be integrated into a known medical imaging viewer to changethe orientation of the altered image. Through the medical imagingviewer, the user may view the altered image and determine how thealtered image may appear as another form of visual image (e.g., x-ray,radiograph, PET scan, CT scan).

In the present embodiment, the user may dynamically switch or shiftbetween displays of various altered images to show the different stagesor level of severity (i.e., progressions or regressions) of a medicalcondition.

In the present embodiment, the dynamic image alteration program 110 a,110 b may determine the specific alterations for the images. While inthe automated test mode, if the dynamic image alteration program 110 a,110 b determines that the proposed alterations are closely related to abody type, possible diagnostic manifestation or medical condition (i.e.,includes diagnosed or suspected medical condition), then the dynamicimage alteration program 110 a, 110 b may generate altered imagesclosely related to that medical condition or body type.

In the present embodiment, if the received data includes a specificmedical finding (e.g., possible medical diagnosis), then the dynamicimage alteration program 110 a, 110 b may generate altered images, whilein the automated test mode, that are associated with that specificmedical finding. The user may, however, override this function bychanging the dynamic image alteration program 110 a, 110 b settings.

Regardless if the dynamic image alteration program 110 a, 110 bdetermined there was no user input at 208, or the dynamic imagealteration program 110 a, 110 b applied the user input at 210, thedynamic image alteration program 110 a, 110 b proceeds to generate atleast one alteration to the existing image (i.e., altered image) at 212.The dynamic image alteration program 110 a, 110 b may retrieve imagesfrom a known deep learning and image analysis engine with a medicalknowledge base (e.g., PACS) based on the previously determined userrules at 206 and/or user input at 210. The dynamic image alterationprogram 110 a, 110 b may then utilize a known generative machinelearning network to dynamically apply the changes displayed in theretrieved images from the PACS to the existing images specificallyrelated to the data received on the specific patient to generate atleast one altered image. The altered images may be anatomically altered,rather than an immaterial adjustment (e.g., change of window, level,color or filter), to create new anatomical findings. An adversarialneural network may be established for creating simulated altered images.The dynamic image alteration program 110 a, 110 b may then utilize asample (i.e., at least one database image) from the medical knowledgebase (e.g., database 114) of the deep learning and image analysisengine, as well as a sample from a known generator (e.g., a deep neuralnetwork). Both samples may be fed into a known discriminator (i.e., aconvolutional neural network that is a parameterized function thatattempts to distinguish between the samples from the real images and thealtered images). Each altered and existing image may be presented to theuser, or alternatively, the altered and existing images may be returnedto the cloud storage service.

Continuing the previous example, the dynamic image alteration program110 a, 110 b retrieves images from the medical knowledge base of thePACS, which are associated with the progression of anaplasticastrocytoma in women between the ages 30 and 50, glioblastoma grade IVin women between the ages 30 and 50, and the presence of anaplasticastrocytoma in the left cerebral hemisphere, the brain stem and thespinal cord in women between the ages of 30 and 50. From the medicalknowledge base of the PACS, user rules, and user input, the dynamicimage alteration program 110 a, 110 b retrieves 68 different images. Thedynamic image alteration program 110 a, 110 b then utilizes thegenerative machine learning network to dynamically apply the changesthereby altering the 12 patient specific scans to show how anaplasticastrocytoma progresses, the presence of glioblastoma grade IV, and thepresence or spread of anaplastic astrocytoma in the left cerebralhemisphere, the brain stem, the spinal cord and other organ systemsoutside of the central nervous system as it relates to this specific 39year old female patient. The dynamic image alteration program 110 a, 110b then generates 157 different altered images related to the specificpatient based on the user input and rules. Of the 157 altered imagesgenerated by dynamic image alteration program 110 a, 110 b, 45 alteredimages show how the anaplastic astrocytoma progresses in the specificpatient for the next five years, 80 altered images show the presence ofglioblastoma grade IV at different stages of development in the specificpatient, and 32 altered images show the presence of anaplasticastrocytoma in the left cerebral hemisphere, brain stem and spinal cord,as well as in the rare cases, the spread of anaplastic astrocytoma tothe skeletal system (i.e., bones) and respiratory system (i.e., lungs).

In the present embodiment, the dynamic image alteration program 110 a,110 b may retrieve images from the medical knowledge base with similardemographic information as the patient from the received medical data.The retrieved images may be utilized to generate altered images. If,however, the user prefers to change these settings, then, the settingsmay be modified by a system administrator. Additionally, the retrievedand altered images may be utilized by the dynamic image alterationprogram 110 a, 110 b while the dynamic image alteration program 110 a,110 b is in automated test mode.

In the present embodiment, the altered images may be presented to theuser. The dynamic image alteration program 110 a, 110 b may present thealtered image on a screen for the user to review. The altered image maybe proximally displayed to the user (e.g., side-by-side or in a stack).Each altered image may include a brief description under the imageindicating the alteration and user rules utilized to generate thealtered image, and other important details related to the altered image(e.g., time-period, type of scan, orientation). For example, the alteredimages are displayed on the screen for the user to proximally review.The user sequentially reviews each of the altered images where theimages related to the progression of the specific disease are organizedfrom the least to most progressed stage or severity, and a briefdescription is included with each altered image explaining thetime-period, and alteration and/or user rules applied to generate thealtered image.

In the present embodiment, two or more altered images may be organizedbased on type of alteration implemented to generate the image, andtime-period or interval associated with the altered image (i.e., defaultsettings). Additionally, two or more altered images may be proximallydisplayed to the user (e.g., side-by-side or in a stack). If, however,the user prefers to change the default settings, then the settings maybe modified by a system administrator.

Then, at 214, the dynamic image alteration program 110 a, 110 bdetermines if the user has additional user input. The dynamic imagealteration program 110 a, 110 b may obtain the additional user inputusing the client computer 102 and prompt the user to provide additionaluser input to the dynamic image alteration program 110 a, 110 b. Afterthe altered image is displayed, the user may be prompted (e.g., via adialog box) to include additional alterations that the user failed topreviously include. The dialog box, for example, may ask the userwhether there is any additional user input not previously included byuser. Under the question, there may be “Yes” and “No” buttons for theuser to select.

If the dynamic image alteration program 110 a, 110 b determines that theuser has no additional user input at 214, then the dynamic imagealteration program 110 a, 110 b may conclude. The user may then continueto review the altered image, and any existing or comparative imagesgenerated for the specific patient.

Continuing the previous example, after the 157 altered images aregenerated and displayed for the user, a dialog box appears askingwhether the user has any additional user input. The resident clicked the“No” button under the question in the dialog box. The dialog box thendisappears and the resident continues to review the altered images.

If the dynamic image alteration program 110 a, 110 b determines that theuser has additional user input at 214, then the dynamic image alterationprogram 110 a, 110 b may return to 210 to apply user input. The dynamicimage alteration program 110 a, 110 b may utilize the additional userinput to generate secondary altered images.

Continuing the previous example, if the resident selected the “Yes”button in the dialog box to provide additional user input, then thedynamic image alteration program 110 a, 110 b returns to 210 to applythe user input.

In the present embodiment, the user may compare altered and existingimages in the dynamic image alteration program 110 a, 110 b. At the topof the altered image, there may be a “Compare” button for the user tocompare multiple images. If the user clicks the “Compare” button, theuser may be prompted (e.g., via a dialog box) to compare one or multiplegenerated images (i.e., existing images received from the patient'sdata, or altered images based on one or more user rules or input) withthe altered image. The dialog box, for example, may include two buttons,“Existing Image(s)” or “Altered Image(s).” If the user selects the“Existing Image(s)” button, then the user may select to compare thealtered image with at least one existing image received from thepatient's data. Another dialog box may appear in which the existingimages received from the patient's data may be included for the user toreview and select at least one existing image to review and compare withthe altered image. If, however, the user selects “Altered Image,” thenthe user may compare the altered image with another altered image basedon at least one user rule or alteration selected at 214 (i.e.,“comparable image”). Another dialog box, for example, may appear inwhich the user rules and alterations are listed, and the user may selectat least one user rule or alteration to exclude from the comparableimage. Once the user selected at least one user rule or alteration, theuser may click the “Submit” button located at the bottom of the dialogbox. Then the dialog box may disappear. The user may utilize the dynamicimage alteration program 110 a, 110 b for a proximal comparison (e.g.,side-by-side or in a stack) of the comparable or existing image with thealtered image.

The present embodiment may include alternate means (e.g., audio command,direct screen editing of the existing image) for the user provide userrules or input. The user may indicate the alternate means, for example,by selecting the “Command” button located at the bottom of the screen.Alternatively, the means of providing user rules or input may depend onthe computerized medical image display program utilized by the user.

The present embodiment may include an option for the user to save all orsome of the images generated (e.g., altered, comparable or existingimages). The saved images may be saved onto computer-readable tangiblestorage devices, portable computer-readable tangible storage devices, ora user's computer 102 for research and training purposes, or review bythe user at a later time. The saved images may be indexed to limitaccess to certain users based on the role or rights of the user. Therights of the user may be previously set by the user and may be approvedby the system administrator. Additionally, the altered images may beindexed based on how each image is altered. According to at least oneembodiment, the saved images may be stored as a reference image in amedical knowledge base.

In the present embodiment, the dynamic image alteration program 110 a,110 b and the deep learning and image analysis engine with a medicalknowledge base may utilize natural language processing (NLP) to processuser rules and input for retrieving and altering the existing image.

The present embodiment may be utilized to alter non-static images (e.g.,video or movies) to train the user to rapidly respond to images invarious domains (e.g., pilots, fire fighters, police officers,physicians, drivers).

It may be appreciated that FIG. 2 provides only an illustration of oneembodiment and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted embodiment(s) may be made based on design and implementationrequirements.

FIG. 3 is a block diagram 900 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.3 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Data processing system 902, 904 is representative of any electronicdevice capable of executing machine-readable program instructions. Dataprocessing system 902, 904 may be representative of a smart phone, acomputer system, PDA, or other electronic devices. Examples of computingsystems, environments, and/or configurations that may represented bydata processing system 902, 904 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, network PCs, minicomputer systems, anddistributed cloud computing environments that include any of the abovesystems or devices.

User client computer 102 and network server 112 may include respectivesets of internal components 902 a, b and external components 904 a, billustrated in FIG. 3. Each of the sets of internal components 902 a, bincludes one or more processors 906, one or more computer-readable RAMs908 and one or more computer-readable ROMs 910 on one or more buses 912,and one or more operating systems 914 and one or more computer-readabletangible storage devices 916. The one or more operating systems 914, thesoftware program 108 and the dynamic image alteration program 110 a inclient computer 102, and the dynamic image alteration program 110 b innetwork server 112, may be stored on one or more computer-readabletangible storage devices 916 for execution by one or more processors 906via one or more RAMs 908 (which typically include cache memory). In theembodiment illustrated in FIG. 3, each of the computer-readable tangiblestorage devices 916 is a magnetic disk storage device of an internalhard drive. Alternatively, each of the computer-readable tangiblestorage devices 916 is a semiconductor storage device such as ROM 910,EPROM, flash memory or any other computer-readable tangible storagedevice that can store a computer program and digital information.

Each set of internal components 902 a, b also includes a R/W drive orinterface 918 to read from and write to one or more portablecomputer-readable tangible storage devices 920 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 and the dynamic image alteration program 110 a and 110 b canbe stored on one or more of the respective portable computer-readabletangible storage devices 920, read via the respective R/W drive orinterface 918 and loaded into the respective hard drive 916.

Each set of internal components 902 a, b may also include networkadapters (or switch port cards) or interfaces 922 such as a TCP/IPadapter cards, wireless Wi-Fi interface cards, or 3G or 4G wirelessinterface cards or other wired or wireless communication links. Thesoftware program 108 and the dynamic image alteration program 110 a inclient computer 102 and the dynamic image alteration program 110 b innetwork server computer 112 can be downloaded from an external computer(e.g., server) via a network (for example, the Internet, a local areanetwork or other, wide area network) and respective network adapters orinterfaces 922. From the network adapters (or switch port adaptors) orinterfaces 922, the software program 108 and the dynamic imagealteration program 110 a in client computer 102 and the dynamic imagealteration program 110 b in network server computer 112 are loaded intothe respective hard drive 916. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 904 a, b can include a computerdisplay monitor 924, a keyboard 926, and a computer mouse 928. Externalcomponents 904 a, b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 902 a, b also includes device drivers930 to interface to computer display monitor 924, keyboard 926, andcomputer mouse 928. The device drivers 930, R/W drive or interface 918and network adapter or interface 922 comprise hardware and software(stored in storage device 916 and/or ROM 910).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., fl networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Analytics as a Service (AaaS): the capability provided to the consumeris to use web-based or cloud-based networks (i.e., infrastructure) toaccess an analytics platform. Analytics platforms may include access toanalytics software resources or may include access to relevantdatabases, corpora, servers, operating systems or storage. The consumerdoes not manage or control the underlying web-based or cloud-basedinfrastructure including databases, corpora, servers, operating systemsor storage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, illustrative cloud computing environment 1000is depicted. As shown, cloud computing environment 1000 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1000A, desktop computer 1000B, laptopcomputer 1000C, and/or automobile computer system 1000N may communicate.Nodes 100 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1000to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1000A-N shown in FIG. 4 are intended to be illustrative only and thatcomputing nodes 100 and cloud computing environment 1000 can communicatewith any type of computerized device over any type of network and/ornetwork addressable connection (e.g., using a web browser).

Referring now to FIG. 5, a set of functional abstraction layers 1100provided by cloud computing environment 1000 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 1102 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1104;RISC (Reduced Instruction Set Computer) architecture based servers 1106;servers 1108; blade servers 1110; storage devices 1112; and networks andnetworking components 1114. In some embodiments, software componentsinclude network application server software 1116 and database software1118.

Virtualization layer 1120 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1122; virtual storage 1124; virtual networks 1126, including virtualprivate networks; virtual applications and operating systems 1128; andvirtual clients 1130.

In one example, management layer 1132 may provide the functionsdescribed below. Resource provisioning 1134 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1136provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1138 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1140provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1142 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1144 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 1146; software development and lifecycle management 1148;virtual classroom education delivery 1150; data analytics processing1152; transaction processing 1154; and dynamic image alteration 1156. Adynamic image alteration program 110 a, 110 b provides a way todynamically alter an image based on user rules and actions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for dynamically altering at least oneimage, comprising: receiving a plurality of data, wherein the receivedplurality of data includes at least one existing medical image;determining that one or more user instructions for the received existingimage were received; implementing the one or more user instructions onthe received existing medical image; and altering the received existingmedical image based on the one or more implemented user instructions anda medical knowledge base, wherein in response to determining at leastone medical condition in connection with the received plurality of data,determining a plurality of progressions and a plurality of regressionsbased on the determined medical condition, wherein the existing medicalimage is determined based on the determined plurality of progressionsand determined plurality of regressions to the determined medicalcondition, wherein the user dynamically switches the generated pluralityof progressions and the generated plurality of regressions of thereceived existing medical image.
 2. The method of claim 1, furthercomprising: presenting the altered one or more images to the user,wherein the altered one or more images are proximally presented to theuser.
 3. The method of claim 1, wherein determining that one or moreuser instructions for the received existing image were received, furthercomprises: determining that one or more configurable rules for thereceived existing image were received; implementing the determined oneor more configurable rules on the received existing medical image; andaltering the received existing medical image based on the implementedone or more configurable rules and the medical knowledge base.
 4. Themethod of claim 1, wherein determining that one or more userinstructions for the received existing image were received, furthercomprises: determining that one or more user input for the receivedexisting image were received; implementing the determined one or moreuser input on the received existing medical image; and altering thereceived existing medical image based on the implemented one or moreuser input and the medical knowledge base.
 5. The method of claim 1,further comprising: receiving at least one specific disease typeselected by the user; and altering the at least one existing medicalimage, wherein the altered at least one existing medical image depictsthe received specific disease type.
 6. The method of claim 1, furthercomprising: receiving an automated test mode request from the user;determining at least one possible diagnoses based on the receivedexisting medical image and the medical knowledge base; generating aplurality of computer-generated images based on the received existingmedical image by utilizing the medical knowledge base, wherein thegenerated plurality of computer-generated images includes the determinedat least one possible diagnoses; and presenting, to the user, thegenerated plurality of computer-generated images, wherein the generatedplurality of computer-generated images assists with the identificationof a plurality of specific medical findings.
 7. The method of claim 1,further comprising: receiving an automated test mode request by theuser; generating a plurality of computer-generated images based on theimplemented one or more configurable rules on the received existingmedical image; and presenting, to the user, the generated plurality ofcomputer-generated images, wherein the generated plurality ofcomputer-generated images assists to identify a plurality of specificmedical findings, and wherein the generated plurality ofcomputer-generated images continue until a threshold is exceeded.
 8. Themethod of claim 1, further comprising: receiving at least one specificanatomical region selected by the user; determining at least one medicalcondition in connection with the received plurality of data; andaltering at least one existing medical image, wherein the altered atleast one existing medical image depicts the selected at least onespecific anatomical region and the determined medical condition.
 9. Themethod of claim 1, further comprising: determining at least one medicalcondition associated with the received plurality of data; retrieving,from the medical knowledge base, at least one reference image inconnection with the determined medical condition; and altering thereceived existing medical image based on the retrieved at least onereference image.
 10. A computer system for dynamically altering at leastone image, comprising: one or more processors, one or morecomputer-readable memories, one or more computer-readable tangiblestorage medium, and program instructions stored on at least one of theone or more tangible storage medium for execution by at least one of theone or more processors via at least one of the one or more memories,wherein the computer system is capable of performing a methodcomprising: receiving a plurality of data, wherein the receivedplurality of data includes at least one existing medical image;determining that one or more user instructions for the received existingimage were received; implementing the one or more user instructions onthe received existing medical image; and altering the received existingmedical image based on the one or more implemented user instructions anda medical knowledge base, wherein in response to determining at leastone medical condition in connection with the received plurality of data,determining a plurality of progressions and a plurality of regressionsbased on the determined medical condition, wherein the existing medicalimage is determined based on the determined plurality of progressionsand determined plurality of regressions to the determined medicalcondition, wherein the user dynamically switches the generated pluralityof progressions and the generated plurality of regressions of thereceived existing medical image.
 11. The computer system of claim 10,further comprising: presenting the altered one or more images to theuser, wherein the altered one or more images are proximally presented tothe user.
 12. The computer system of claim 10, wherein determining thatone or more user instructions for the received existing image werereceived, further comprises: determining that one or more configurablerules for the received existing image were received; implementing thedetermined one or more configurable rules on the received existingmedical image; and altering the received existing medical image based onthe implemented one or more configurable rules and the medical knowledgebase.
 13. The computer system of claim 10, wherein determining that oneor more user instructions for the received existing image were received,further comprises: determining that one or more user input for thereceived existing image were received; implementing the determined oneor more user input on the received existing medical image; and alteringthe received existing medical image based on the implemented one or moreuser input and the medical knowledge base.
 14. The computer system ofclaim 10, further comprising: receiving at least one specific diseasetype selected by the user; and altering the at least one existingmedical image, wherein the altered at least one existing medical imagedepicts the received specific disease type.
 15. The computer system ofclaim 10, further comprising: receiving an automated test mode requestfrom the user; determining at least one possible diagnoses based on thereceived existing medical image and the medical knowledge base;generating a plurality of computer-generated images based on thereceived existing medical image by utilizing the medical knowledge base,wherein the generated plurality of computer-generated images includesthe determined at least one possible diagnoses; and presenting, to theuser, the generated plurality of computer-generated images, wherein thegenerated plurality of computer-generated images assists with theidentification of a plurality of specific medical findings.
 16. Thecomputer system of claim 10, further comprising: receiving an automatedtest mode request by the user; generating a plurality ofcomputer-generated images based on the implemented one or moreconfigurable rules on the received existing medical image; andpresenting, to the user, the generated plurality of computer-generatedimages, wherein the generated plurality of computer-generated imagesassists to identify a plurality of specific medical findings, andwherein the generated plurality of computer-generated images continueuntil a threshold is exceeded.
 17. A computer program product fordynamically altering at least one image, comprising: one or morecomputer-readable storage media and program instructions stored on atleast one of the one or more tangible storage media, the programinstructions executable by a processor to cause the processor to performa method comprising: receiving a plurality of data, wherein the receivedplurality of data includes at least one existing medical image;determining that one or more user instructions for the received existingimage were received; implementing the one or more user instructions onthe received existing medical image; and altering the received existingmedical image based on the one or more implemented user instructions anda medical knowledge base, wherein in response to determining at leastone medical condition in connection with the received plurality of data,determining a plurality of progressions and a plurality of regressionsbased on the determined medical condition, wherein the existing medicalimage is determined based on the determined plurality of progressionsand determined plurality of regressions to the determined medicalcondition, wherein the user dynamically switches the generated pluralityof progressions and the generated plurality of regressions of thereceived existing medical image.
 18. The computer program product ofclaim 17, wherein determining that one or more user instructions for thereceived existing image were received, further comprises: determiningthat one or more configurable rules for the received existing image werereceived; implementing the determined one or more configurable rules onthe received existing medical image; and altering the received existingmedical image based on the implemented one or more configurable rulesand the medical knowledge base.
 19. The computer program product ofclaim 17, wherein determining that one or more user instructions for thereceived existing image were received, further comprises: determiningthat one or more user input for the received existing image werereceived; implementing the determined one or more user input on thereceived existing medical image; and altering the received existingmedical image based on the implemented one or more user input and themedical knowledge base.